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Digitalization of urban multi-energy systems – Advances in digital twin applications across life-cycle phases 城市多能源系统的数字化--数字孪生在生命周期各阶段的应用进展
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-10-28 DOI: 10.1016/j.adapen.2024.100196
Urban multi-energy systems (UMES) incorporating distributed energy resources are vital to future low-carbon energy systems. These systems demand complex solutions, including increased integration of renewables, improved efficiency through electrification, and exploitation of synergies via sector coupling across multiple sectors and infrastructures. Digitalization and the Internet of Things bring new opportunities for the design-build-operate workflow of the cyber-physical urban multi-energy systems. In this context, digital twins are expected to play a crucial role in managing the intricate integration of assets, systems, and actors within urban multi-energy systems. This review explores digital twin opportunities for urban multi-energy systems by first considering the challenges of urban multi energy systems. It then reviews recent advancements in digital twin architectures, energy system data categories, semantic ontologies, and data management solutions, addressing the growing data demands and modelling complexities. Digital twins provide an objective and comprehensive information base covering the entire design, operation, decommissioning, and reuse lifecycle phases, enhancing collaborative decision-making among stakeholders. This review also highlights that future research should focus on scaling digital twins to manage the complexities of urban environments. A key challenge remains in identifying standardized ontologies for seamless data exchange and interoperability between energy systems and sectors. As the technology matures, future research is required to explore the socio-economic and regulatory implications of digital twins, ensuring that the transition to smart energy systems is both technologically sound and socially equitable. The paper concludes by making a series of recommendations on how digital twins could be implemented for urban multi energy systems.
包含分布式能源资源的城市多能源系统(UMES)对未来的低碳能源系统至关重要。这些系统需要复杂的解决方案,包括增加可再生能源的集成度、通过电气化提高效率,以及通过多个部门和基础设施之间的部门耦合利用协同效应。数字化和物联网为网络-物理城市多能源系统的设计-建造-运行工作流程带来了新的机遇。在此背景下,数字孪生有望在管理城市多能源系统中资产、系统和参与者的复杂集成方面发挥关键作用。本综述首先探讨了城市多能源系统所面临的挑战,从而探讨了城市多能源系统的数字孪生机遇。然后回顾数字孪生架构、能源系统数据类别、语义本体和数据管理解决方案的最新进展,以应对日益增长的数据需求和建模复杂性。数字孪生提供了一个客观、全面的信息库,涵盖了整个设计、运行、退役和再利用生命周期的各个阶段,加强了利益相关者之间的协同决策。本综述还强调,未来的研究应侧重于扩大数字孪生的规模,以管理城市环境的复杂性。一个关键的挑战仍然是确定标准化的本体,以实现能源系统和部门之间的无缝数据交换和互操作性。随着技术的成熟,未来的研究需要探索数字孪生的社会经济和监管影响,确保向智能能源系统的过渡在技术上是合理的,在社会上是公平的。本文最后就如何在城市多能源系统中实施数字孪生提出了一系列建议。
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引用次数: 0
Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China 基于土地利用和可解释机器学习的多尺度用电预测模型:中国案例研究
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-10-28 DOI: 10.1016/j.adapen.2024.100197
The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land use was proposed. This model achieves land-use subdivision to provide highly correlated variables; exhibits strong interpretability, thereby revealing even marginal effects of land use on electricity consumption; and demonstrates high performance, thereby enabling large-scale simulations and predictions. Using 297 cities and 2,505 counties as case studies, the key findings are as follows: (1) The model demonstrates strong generalization ability (R2 = 0.91), high precision (Kappa = 0.77), and robustness, with an overall prediction accuracy exceeding 80 %; (2) The marginal impact of industrial land on electricity consumption is more complex, with more efficiency achieved by limiting its area to either 104.3 km2 or between 288.2 and 657.3 km2; (3) The marginal impact of commercial and residential land on electricity consumption exhibits a strong linear relationship (R2 > 0.80). Restricting the scale to 11.3 km2 could effectively mitigate this impact. Mixed commercial and residential land is advantageous for overall electricity consumption control, but after exceeding 43.5 km2, separate layout considerations for urban residential land are necessary; (4) In 2030, Shanghai's electricity consumption is projected to reach 155,143 million kW·h, making it the highest among the 297 cities. Meanwhile, Suzhou Industrial Park leads among the 2,505 districts with a consumption of 30,996 million kW·h; (5) Identify future electricity consumption hotspots and clustering characteristics, evaluate the renewable energy potential in these hotspot areas, and propose targeted strategies accordingly.
用电量预测在促进可持续发展、确保能源安全和弹性、促进区域规划以及整合可再生能源方面发挥着至关重要的作用。本文提出了一种基于土地利用的新型用电特征描述和预测模型。该模型实现了土地利用的细分,提供了高度相关的变量;表现出很强的可解释性,从而揭示了土地利用对用电量的边际效应;并表现出很高的性能,从而实现了大规模的模拟和预测。以 297 个城市和 2,505 个县作为案例研究,主要发现如下:(1) 模型具有较强的泛化能力(R2 = 0.91)、较高的精度(Kappa = 0.77)和稳健性,总体预测精度超过 80%;(2) 工业用地对用电量的边际影响较为复杂,将其面积限制在 104.3 平方公里或 288.2 至 657.3 平方公里之间可提高效率;(3) 商业用地和住宅用地对用电量的边际影响呈现出较强的线性关系(R2 >0.80)。将规模限制在 11.3 平方公里可有效缓解这一影响。商住混合用地对整体用电控制有利,但超过 43.5 km2 后,城市居住用地的布局需要单独考虑;(4)预计 2030 年,上海用电量将达到 1551.43 亿 kW-h,在 297 个城市中居首位。同时,苏州工业园区的用电量为 309.96 亿 kW-h,在 2,505 个区中居首位;(5)确定未来的用电热点和集群特征,评估这些热点地区的可再生能源潜力,并提出相应的针对性策略。
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引用次数: 0
Green light for bidirectional charging? Unveiling grid repercussions and life cycle impacts 为双向充电开绿灯?揭示电网反响和生命周期影响
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-10-24 DOI: 10.1016/j.adapen.2024.100195
Bidirectional charging, such as Vehicle-to-Grid, is increasingly seen as a way to integrate the growing number of battery electric vehicles into the energy system. The electrical storage capacity in the system can be enhanced by using electric vehicles as flexible storage units. However, large-scale applications of Vehicle-to-Grid may require significant expansion of distribution grids. Previous studies lack a comprehensive environmental assessment of related impacts. Contributing to this research gap, this article combines techno-economic grid simulations with scenario-based Life Cycle Assessments. The case study focuses on rural distribution grids in Southern Germany, projecting the repercussions of different charging scenarios by 2040. Besides a Vehicle-to-Grid scenario, a mixed scenario of Vehicle-to-Home, Vehicle-to-Grid, and direct charging is investigated. Results indicate that Vehicle-to-Grid charging increases grid impacts due to higher charging simultaneities and power losses, especially when following spot market prices. Despite these challenges, the secondary use of battery electric vehicles as storage units can offset adverse environmental effects. Bidirectional charging allows for higher use of volatile renewable energies and can accelerate their integration into the power system. When considering these diverse environmental effects, bidirectional charging scenarios show overall lower impacts on climate change per battery electric vehicle compared to direct charging. The insights provided are valuable for researchers, industry, utilities, and policymakers to understand the potential positive and negative impacts of large-scale battery electric vehicle integration. The article highlights the most influential parameters that should be considered before large-scale penetration.
双向充电(如 "车辆到电网")越来越多地被视为将越来越多的电池电动汽车纳入能源系统的一种方式。利用电动汽车作为灵活的存储单元,可以提高系统的电力存储容量。然而,大规模应用 "车联网 "可能需要大幅扩展配电网。以往的研究缺乏对相关影响的全面环境评估。为了弥补这一研究空白,本文将技术经济电网模拟与基于情景的生命周期评估相结合。案例研究以德国南部的农村配电网为重点,预测了到 2040 年不同充电方案的影响。除了 "车辆到电网 "方案外,还研究了 "车辆到家庭"、"车辆到电网 "和直接充电的混合方案。结果表明,车辆到电网充电会增加对电网的影响,因为充电同时性更高,电能损耗也更大,尤其是在遵循现货市场价格的情况下。尽管存在这些挑战,但二次使用电池电动汽车作为存储单元可以抵消对环境的不利影响。双向充电允许更多使用不稳定的可再生能源,并能加速其融入电力系统。考虑到这些不同的环境影响,与直接充电相比,双向充电方案显示每辆电池电动汽车对气候变化的总体影响较低。文章提供的见解对研究人员、工业、公用事业和政策制定者了解大规模电池电动汽车集成的潜在正面和负面影响非常有价值。文章强调了在大规模普及之前应考虑的最具影响力的参数。
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引用次数: 0
MANGOever: An optimization framework for the long-term planning and operations of integrated electric vehicle and building energy systems MANGOever:集成电动汽车和建筑能源系统长期规划和运营的优化框架
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-10-22 DOI: 10.1016/j.adapen.2024.100193
The growing electrification of heating and mobility has increased the interdependence of these two sectors and introduced a new coupling with the electricity sector. However, existing studies on local energy planning often focus solely on solutions to meet buildings’ energy demands, neglecting or highly simplifying new mobility demands. Here, we address this gap by introducing MANGOever (Multi-stAge eNerGy Optimization for electric vehicles and energy retrofits), a comprehensive optimization framework for long-term co-planning of building energy systems and electric vehicle (EV) charging infrastructure. The framework optimizes multi-stage investments and operational strategies to minimize system costs and CO2 emissions over a multi-year horizon, considering the stochastic nature of EV charging based on observed driver habits and travel patterns. Applying the model to a case study of a multi-family home in Switzerland reveals significant synergies between EV charging and the management of solar photovoltaic generation. The results underscore the importance of considering habit-based EV charging behavior in the model and demonstrate how diverse EV plug-in behaviors can be leveraged to maximize the use of midday solar production and reduce emissions. These findings emphasize the need for integrated planning of these sectors to achieve a cost-effective, low-carbon energy transition.
供暖和交通日益电气化,增加了这两个部门的相互依存性,并与电力部门产生了新的联系。然而,现有的地方能源规划研究往往只关注满足建筑物能源需求的解决方案,忽视或高度简化了新的交通需求。在此,我们引入了 MANGOever(电动汽车和能源改造的多阶段 eNerGy 优化)来弥补这一不足,它是一个综合优化框架,用于建筑能源系统和电动汽车(EV)充电基础设施的长期共同规划。该框架根据观察到的驾驶员习惯和出行模式,考虑到电动汽车充电的随机性,对多阶段投资和运营策略进行优化,以在多年期限内最大限度地降低系统成本和二氧化碳排放量。将该模型应用于瑞士的一个多户住宅案例研究,发现电动汽车充电与太阳能光伏发电管理之间存在显著的协同效应。研究结果强调了在模型中考虑基于习惯的电动汽车充电行为的重要性,并展示了如何利用不同的电动汽车插电行为最大限度地利用中午的太阳能发电并减少排放。这些发现强调了对这些部门进行综合规划的必要性,以实现具有成本效益的低碳能源转型。
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引用次数: 0
Reviewing the complexity of endogenous technological learning for energy system modeling 回顾能源系统建模中内生技术学习的复杂性
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-10-19 DOI: 10.1016/j.adapen.2024.100192
Energy system components like renewable energy technologies or electrolyzers are subject to decreasing investment costs driven by technological progress. Various methods have been developed in the literature to capture model-endogenous technological learning. This review demonstrates the non-linear relationship between investment costs and production volume, resulting in non-convex optimization problems and discuss concepts to account for technological progress. While iterative solution methods tend to find future energy system designs that rely on suboptimal technology mixes, exact solutions leading to global optimality are computationally demanding. Most studies omit important system aspects such as sector integration, or a detailed spatial, temporal, and technological resolution to maintain model solvability, which likewise distorts the impact of technological learning. This can be improved by the application of methods such as temporal or spatial aggregation, decomposition methods, or the clustering of technologies. This review reveals the potential of those methods and points out important considerations for integrating endogenous technological learning. We propose a more integrated approach to handle computational complexity when integrating technological learning, that aims to preserve the model's feasibility. Furthermore, we identify significant gaps in current modeling practices and suggest future research directions to enhance the accuracy and utility of energy system models.
可再生能源技术或电解槽等能源系统组件的投资成本受技术进步的驱动而不断降低。文献中提出了各种方法来捕捉模型内生的技术学习。本综述论证了投资成本与产量之间的非线性关系,这导致了非凸优化问题,并讨论了考虑技术进步的概念。虽然迭代求解方法往往能找到依赖次优技术组合的未来能源系统设计,但实现全局最优的精确求解方法对计算要求很高。大多数研究忽略了重要的系统方面,如部门整合或详细的空间、时间和技术分辨率,以保持模型的可解决性,这同样扭曲了技术学习的影响。应用时空聚合、分解方法或技术聚类等方法可以改善这种情况。本综述揭示了这些方法的潜力,并指出了整合内生技术学习的重要考虑因素。我们提出了一种更综合的方法,用于处理整合技术学习时的计算复杂性,旨在保持模型的可行性。此外,我们还指出了当前建模实践中存在的重大差距,并提出了未来的研究方向,以提高能源系统模型的准确性和实用性。
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引用次数: 0
A review of mixed-integer linear formulations for framework-based energy system models 基于框架的能源系统模型的混合整数线性公式综述
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-09-20 DOI: 10.1016/j.adapen.2024.100190
Optimization-based frameworks for energy system modeling such as TIMES, ETHOS.FINE, or PyPSA have emerged as important tools to outline a cost-efficient energy transition. Consequently, numerous reviews have compared the capabilities and application cases of established energy system optimization frameworks with respect to their model features or adaptability but widely neglect the frameworks’ underlying mathematical structure. This limits their added value for users who not only want to use models but also program them themselves.
To address this issue, we follow a hybrid approach by not only reviewing 63 optimization-based frameworks for energy system modeling with a focus on their mathematical implementation but also conducting a meta-review of 68 existing literature reviews.
Our work reveals that the basic concept of network-based energy flow optimization has remained the same since the earliest publications in the 1970s. Thereby, the number of open-source available optimization frameworks for energy system modeling has more than doubled in the last ten years, mainly driven by the uptake of energy transition and progress in computer-aided optimization.
To go beyond a qualitative discussion, we also define the mathematical formulation for a mixed-integer optimization model comprising all the model features discussed in this work. We thereby aim to facilitate the implementation of future object-oriented frameworks and to increase the comprehensibility of existing ones for energy system modelers.
基于优化的能源系统建模框架(如 TIMES、ETHOS.FINE 或 PyPSA)已成为勾勒具有成本效益的能源转型的重要工具。因此,许多评论都比较了现有能源系统优化框架在模型功能或适应性方面的能力和应用案例,但普遍忽视了框架的底层数学结构。为了解决这个问题,我们采用了一种混合方法,不仅对 63 个基于优化的能源系统建模框架进行了综述,重点关注其数学实现,而且还对 68 篇现有文献综述进行了元综述。我们的工作表明,自 20 世纪 70 年代最早发表以来,基于网络的能源流优化的基本概念一直未变。因此,在过去十年中,能源系统建模的开源优化框架数量翻了一番还多,这主要是受能源转型和计算机辅助优化技术进步的推动。为了超越定性讨论,我们还定义了一个混合整数优化模型的数学公式,该模型包含了本文讨论的所有模型特征。因此,我们的目标是促进未来面向对象框架的实施,并提高现有框架对能源系统建模人员的可理解性。
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引用次数: 0
Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data 推进电动汽车的健康状况评估:基于变压器的利用真实世界数据的方法
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-09-04 DOI: 10.1016/j.adapen.2024.100188

The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processes raw time-series battery and vehicle-related data from a single EV trip to estimate the SOH. Unlike conventional methods that rely on lab-experimented battery cycle data, SOH-TEC utilizes real-world EV operation data, enhancing practical application. The model is trained and evaluated on a real-world dataset collected over nearly three years from three EVs. This dataset includes reliable SOH labels obtained through periodic constant-current full-discharge tests using a chassis dynamometer. Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. Moreover, our proposed pre-training strategies with unlabeled data, particularly SOH ordinal comparison, significantly enhance the model’s performance; using only 50% of the labeled data achieves results nearly identical to those obtained with the full dataset. Self-attention map analysis reveals that the model primarily focuses on stationary or consistent driving periods to estimate SOH. While the study is constrained by a dataset featuring repetitive driving patterns, it highlights the significant potential of transformer for SOH estimation in EVs and offers valuable insights for future data collection and model development.

电动汽车(EV)的广泛应用凸显了对创新方法的迫切需求,以估算其锂离子电池的健康状况(SOH),这对确保安全和效率至关重要。本研究介绍了 SOH-TEC,这是一种基于变压器编码器的模型,可处理来自单次电动汽车行程的原始时间序列电池和车辆相关数据,以估算 SOH。与依赖实验室实验电池循环数据的传统方法不同,SOH-TEC 利用真实世界的电动汽车运行数据,提高了实际应用能力。该模型在近三年来从三辆电动汽车收集的真实世界数据集上进行了训练和评估。该数据集包括通过使用底盘测功机进行定期恒流全放电测试获得的可靠 SOH 标签。尽管嘈杂的电动汽车真实世界数据带来了挑战,但该模型显示出很高的准确性,平均绝对误差为 0.72%,均方根误差为 1.17%。此外,我们提出的使用未标注数据进行预训练的策略,尤其是 SOH 排序比较,显著提高了模型的性能;仅使用 50%的标注数据就能获得与使用完整数据集几乎相同的结果。自我注意力图分析表明,该模型主要侧重于静止或持续驾驶时段来估计 SOH。虽然这项研究受到以重复驾驶模式为特征的数据集的限制,但它强调了变压器在电动汽车 SOH 估算方面的巨大潜力,并为未来的数据收集和模型开发提供了宝贵的见解。
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引用次数: 0
Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling 关于采样成本的主动学习,以提高人工智能建筑能源系统建模能力
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-09-02 DOI: 10.1016/j.adapen.2024.100189

Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.

机器学习被广泛认为是一种有前途的数据驱动建模技术,可用于基于模型的建筑能源系统控制和优化。然而,数据驱动模型的普适性往往面临重大挑战,因为来自建筑运行的可用训练数据通常只涵盖有限的工作条件范围。主动学习可以主动测试未见过的、信息量大的工作条件,通过添加新的数据样本来丰富训练集,从而提高数据驱动模型的泛化性能。本研究开发了一种基于距离和信息密度的新型样本策略,该策略考虑了建筑物运行和室外环境的实时状态。基于马哈拉诺比斯距离,该策略通过评估未标注样本(未见工作状态)与训练样本和其他未标注样本的相似度来确定其采样值。考虑到主动采样过程对建筑能源系统正常运行的不利影响,本文提出了一种基于距离的采样成本指标,用于比较不同采样方法的效率。本文通过对实际高层建筑冷却塔采集的数据进行数据实验,对五种主动学习方法进行了全面深入的比较,其中包括一种结合了基于距离的采样策略的方法。结果表明,主动学习能有效识别信息数据样本,提高数据驱动模型的泛化性能。这些研究成果对于提高人工智能数据驱动的建筑能源系统建模具有重要价值,同时还能大幅降低数据采样成本。
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引用次数: 0
A probabilistic model for real-time quantification of building energy flexibility 实时量化建筑能源灵活性的概率模型
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-08-21 DOI: 10.1016/j.adapen.2024.100186

Buildings have great energy flexibility potential to manage supply-demand imbalance in power grids with high renewable penetration. Accurate and real-time quantification of building energy flexibility is essential not only for engaging buildings in electricity and grid service markets, but also for ensuring the reliable and optimal operation of power grids. This paper proposes a probabilistic model for rapidly quantifying the aggregated flexibility of buildings under uncertainties. An explicit equation is derived as the analytical solution of a commonly used second-order building thermodynamic model to quantify the flexibility of individual buildings, eliminating the need of time-consuming iterative and finite difference computations. A sampling-based uncertainty analysis is performed to obtain the distribution of aggregated building flexibility, considering major uncertainties comprehensively. Validation tests are conducted using 150 commercial buildings in Hong Kong. The results show that the proposed model not only quantifies the aggregated flexibility with high accuracy, but also dramatically reduces the computation time from 3605 s to 6.7 s, about 537 times faster than the existing probabilistic model solved numerically. Moreover, the proposed model is 8 times faster than the archetype-based model and achieves significantly higher accuracy.

在可再生能源渗透率较高的电网中,建筑物在管理供需失衡方面具有巨大的能源灵活性潜力。准确、实时地量化建筑物的能源灵活性不仅对建筑物参与电力和电网服务市场至关重要,而且对确保电网的可靠和优化运行也至关重要。本文提出了一种概率模型,用于在不确定情况下快速量化建筑物的综合灵活性。通过对常用的二阶建筑热力学模型进行分析求解,推导出一个显式方程来量化单个建筑的灵活性,从而省去了耗时的迭代和有限差分计算。在全面考虑主要不确定性的情况下,通过基于抽样的不确定性分析,获得了建筑物总体柔性的分布。利用香港 150 幢商业建筑进行了验证测试。结果表明,所提出的模型不仅能高精度地量化总体柔性,还能将计算时间从 3605 秒大幅缩短至 6.7 秒,比现有的数值概率模型快约 537 倍。此外,所提出的模型比基于原型的模型快 8 倍,而且精度明显更高。
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引用次数: 0
Planning reliable wind- and solar-based electricity systems 规划可靠的风能和太阳能发电系统
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-08-10 DOI: 10.1016/j.adapen.2024.100185

Resource adequacy, or ensuring that electricity supply reliably meets demand, is more challenging for wind- and solar-based electricity systems than fossil-fuel-based ones. Here, we investigate how the number of years of past weather data used in designing least-cost systems relying on wind, solar, and energy storage affects resource adequacy. We find that nearly 40 years of weather data are required to plan highly reliable systems (e.g., zero lost load over a decade). In comparison, this same adequacy could be attained with 15 years of weather data when additionally allowing traditional dispatchable generation to supply 5 % of electricity demand. We further observe that the marginal cost of improving resource adequacy increased as more years, and thus more weather variability, were considered for planning. Our results suggest that ensuring the reliability of wind- and solar-based systems will require using considerably more weather data in system planning than is the current practice. However, when considering the potential costs associated with unmet electricity demand, fewer planning years may suffice to balance costs against operational reliability.

资源充足性,即确保电力供应可靠地满足需求,对于风能和太阳能发电系统来说比化石燃料发电系统更具挑战性。在此,我们研究了在设计依靠风能、太阳能和储能的最低成本系统时,过去气象数据的年数对资源充足性的影响。我们发现,需要近 40 年的天气数据才能规划出高度可靠的系统(例如,十年内零负荷损失)。相比之下,如果允许传统的可调度发电供应 5% 的电力需求,则只需 15 年的气象数据即可达到同样的充足性。我们进一步观察到,随着规划考虑的年份越多,天气变异性越大,提高资源充足性的边际成本也就越高。我们的研究结果表明,要确保风能和太阳能系统的可靠性,就需要在系统规划中使用比目前多得多的天气数据。然而,如果考虑到与未满足电力需求相关的潜在成本,较少的规划年可能就足以平衡成本与运行可靠性之间的关系。
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引用次数: 0
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